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Data, Volume 7, Issue 9 (September 2022) – 16 articles

Cover Story (view full-size image): Renewable energy installations are an integral part of the energy landscape in Germany. However, their presence repeatedly leads to political conflicts with residents, land-use issues or nature conservation. In this context, information on the geographical location and system characteristics of renewable energy installations can be useful in studies on the spatial, social or environmental impacts of renewable energy. This work provides a valid approach to create a dataset of wind turbines, photovoltaic field systems, bioenergy plants, and hydropower plants for Germany based on publicly available data. Established methods (e.g., random forest, image recognition) are used, and new techniques are developed to fill data gaps or to localize installations. View this paper
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11 pages, 4563 KiB  
Data Descriptor
Sheep Nocturnal Activity Dataset
by António Monteiro, Pedro Gonçalves, Maria R. Marques, Ana T. Belo and Fernando Braz
Data 2022, 7(9), 134; https://doi.org/10.3390/data7090134 - 14 Sep 2022
Cited by 5 | Viewed by 2286
Abstract
Monitoring sheep’s behavior is of paramount importance, because deviations from normal patterns may indicate nutritional, thermal or social stress, changes in reproductive status, health issues, or predator attacks. The night period, despite being a more restful period in which animals are theoretically sleeping [...] Read more.
Monitoring sheep’s behavior is of paramount importance, because deviations from normal patterns may indicate nutritional, thermal or social stress, changes in reproductive status, health issues, or predator attacks. The night period, despite being a more restful period in which animals are theoretically sleeping and resting, represents approximately half of the life cycle of animals; therefore, its study is of immense interest. Wearable sensors have become a widely recognized technique for monitoring activity, both for their precision and the ease with which the sensorized data can be analyzed. The present dataset consists of data from the sensorization of 18 Serra da Estrela sheep, during the nocturnal period between 18 November 2021 and 16 February 2022. The data contain measurements taken by ultrasound and accelerometry of the height from neck to ground, as well as measurements taken by an accelerometer in the monitoring collar. Data were collected every 10 s when the animals were in the shelter. With the collection of data from various sensors, active and inactive periods can be identified throughout the night, quantifying the number and average time of those periods. Full article
(This article belongs to the Section Information Systems and Data Management)
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12 pages, 1983 KiB  
Article
Prediction of Retention Indices and Response Factors of Oxygenates for GC-FID by Multilinear Regression
by Nils Kretzschmar, Markus Seifert, Oliver Busse and Jan J. Weigand
Data 2022, 7(9), 133; https://doi.org/10.3390/data7090133 - 14 Sep 2022
Cited by 7 | Viewed by 2981
Abstract
The replacement of fossil carbon sources with green bio-oils promotes the importance of several hundred oxygenated hydrocarbons, which substantially increases the analytical effort in catalysis research. A multilinear regression is performed to correlate retention indices (RIs) and response factors (RFs) with structural properties. [...] Read more.
The replacement of fossil carbon sources with green bio-oils promotes the importance of several hundred oxygenated hydrocarbons, which substantially increases the analytical effort in catalysis research. A multilinear regression is performed to correlate retention indices (RIs) and response factors (RFs) with structural properties. The model includes a variety of possible products formed during the hydrodeoxygenation of bio-oils with good accuracy (RRF2 0.921 and RRI2 0.975). The GC parameters are related to the detailed hydrocarbon analysis (DHA) method, which is commonly used for non-oxygenated hydrocarbons. The RIs are determined from a paraffin standard (C5–C15), and the RFs are calculated with ethanol and 1,3,5-trimethylbenzene as internal standards. The method presented here can, therefore, be used together with the DHA method and be expanded further. In addition to the multilinear regression, an increment system has been developed for aromatic oxygenates, which further improves the prediction accuracy of the response factors with respect to the molecular constitution (R2 0.958). Both predictive models are designed exclusively on structural factors to ensure effortless application. All experimental RIs and RFs are determined under identical conditions. Moreover, a folded Plackett–Burman screening design demonstrates the general applicability of the datasets independent of method- or device-specific parameters. Full article
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12 pages, 1523 KiB  
Data Descriptor
Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device
by Mohamed Elgendi, Valeria Galli, Chakaveh Ahmadizadeh and Carlo Menon
Data 2022, 7(9), 132; https://doi.org/10.3390/data7090132 - 14 Sep 2022
Cited by 8 | Viewed by 6058
Abstract
Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety [...] Read more.
Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety Inventory and Hamilton Anxiety Rating Scale) were used to assess overall anxiety before induction. The data were collected at Simon Fraser University from participants aged 18–56, all of whom were healthy at the time. The ECG and RES signals were collected simultaneously while participants continuously watched video clips that stimulated anxiety-inducing (negative experience) and non-anxiety-inducing events (positive experience). The ECG and RES signals were recorded simultaneously at 500 Hz. The final dataset consisted of psychological scores and physiological signals from 19 participants (14 males and 5 females) who watched eight video clips. This dataset can be used to explore the instantaneous relationship between ECG and RES waveforms and anxiety-inducing video clips to uncover and evaluate the latent characteristic information contained in these biosignals. Full article
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10 pages, 264 KiB  
Data Descriptor
The COLIBAS Study—COVID-19 Lockdown Effects on Mood, Academic Functioning, Alcohol Consumption, and Perceived Immune Fitness: Data from Buenos Aires University Students
by Pauline A. Hendriksen, Pantea Kiani, Agnese Merlo, Analia Karadayian, Analia Czerniczyniec, Silvia Lores-Arnaiz, Gillian Bruce and Joris C. Verster
Data 2022, 7(9), 131; https://doi.org/10.3390/data7090131 - 14 Sep 2022
Cited by 3 | Viewed by 1899
Abstract
A recent study was conducted in the Netherlands to evaluate the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. The study revealed that lockdowns were associated with a [...] Read more.
A recent study was conducted in the Netherlands to evaluate the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. The study revealed that lockdowns were associated with a significantly poorer mood and a reduced perceived immune fitness. Overall, a reduction was seen in alcohol consumption during the lockdown periods. Academic functioning in terms of performance was unaffected; however, a significant reduction in interactions with other students and teachers was reported. There was, however, great variability between students as follows: both an increase and a reduction in alcohol consumption were reported, as well as improvements and poorer academic functioning. The aim of the current online study was to replicate these findings in Argentina. To this extent, a modified version of the survey was conducted among students at the University of Buenos Aires, which was adapted to the local lockdown measures. The survey assessed possible changes in self-reported academic functioning, mood, and health correlates, such as alcohol consumption, perceived immune functioning, and sleep quality compared to before the COVID-19 pandemic. Retrospective assessments were made for four periods, including (1) the period before COVID-19, (2) the first lockdown period (March–December 2020), (3) summer 2021 (January-March 2021, no lockdown), and (4) the second lockdown (from April 2021 to July 2021). This article describes the content of the survey and the corresponding dataset. The survey was completed by 508 participants. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
9 pages, 4420 KiB  
Data Descriptor
Redox Data of Tris(polypyridine)manganese(II) Complexes
by Zinhle Mtshali, Karel G. von Eschwege and Jeanet Conradie
Data 2022, 7(9), 130; https://doi.org/10.3390/data7090130 - 13 Sep 2022
Cited by 1 | Viewed by 1460
Abstract
Very little cyclic voltammetry data for tris(polypyridine)manganese(II) complexes, [MnII(N^N)3]2+, where N^N is bipyridine (bpy), phenanthroline (phen) or substituted bpy or phen ligands, respectively; are available in the literature. Cyclic voltammograms were found for tris(4,7-diphenyl-1,10-phenanthroline)manganese(II) perchlorate [...] Read more.
Very little cyclic voltammetry data for tris(polypyridine)manganese(II) complexes, [MnII(N^N)3]2+, where N^N is bipyridine (bpy), phenanthroline (phen) or substituted bpy or phen ligands, respectively; are available in the literature. Cyclic voltammograms were found for tris(4,7-diphenyl-1,10-phenanthroline)manganese(II) perchlorate only. In addition to our recently published related research article, the data presented here provides cyclic voltammograms and corresponding voltage-current data obtained during electrochemical oxidation and the reduction of four [MnII(N^N)3]2+ complexes, using different scan rates and analyte concentrations. The results show increased concentration and scan rates resulting in higher Mn(II/III) peak oxidation potentials and increased peak current-voltage separations of the irreversible Mn(II/III) redox event. The average peak oxidation and peak reduction potentials of the Mn(II/III) redox events stayed constant within 0.01 V. Similarly, the average of the peak oxidation and reduction potentials of the ligand-based reduction events of [MnII(N^N)3]2+ were constant within 0.01 V. Full article
(This article belongs to the Section Chemoinformatics)
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6 pages, 1188 KiB  
Data Descriptor
Data for Photodissociation of Some Small Molecular Ions Relevant for Astrochemistry and Laboratory Investigation
by Vladimir A. Srećković, Ljubinko M. Ignjatović, Aleksandra Kolarski, Zoran R. Mijić, Milan S. Dimitrijević and Veljko Vujčić
Data 2022, 7(9), 129; https://doi.org/10.3390/data7090129 - 11 Sep 2022
Cited by 2 | Viewed by 1803
Abstract
The calculated photodissociation data of some small molecular ions have been reported. The cross-sections and spectral rate coefficients data have been studied using a quantum mechanical method. The plasma parameters, i.e., conditions, cover temperatures from 1000 to 20,000 K and wavelengths in the [...] Read more.
The calculated photodissociation data of some small molecular ions have been reported. The cross-sections and spectral rate coefficients data have been studied using a quantum mechanical method. The plasma parameters, i.e., conditions, cover temperatures from 1000 to 20,000 K and wavelengths in the EUV and UV region. The influence of temperature and wavelength on the spectral coefficients data of all of the investigated species have been discussed. Data could also be useful for plasma diagnostics in laboratory, astrophysics, and industrial plasmas for their modelling. Full article
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15 pages, 1740 KiB  
Data Descriptor
Geo-Locations and System Data of Renewable Energy Installations in Germany
by David Manske, Lukas Grosch, Julius Schmiedt, Nora Mittelstädt and Daniela Thrän
Data 2022, 7(9), 128; https://doi.org/10.3390/data7090128 - 10 Sep 2022
Cited by 3 | Viewed by 3259
Abstract
Information on geo-locations of renewable energy installations is very useful to investigate spatial, social or environmental questions on their impact at local and national level. However, existing data sets do not provide a sufficiently accurate representation of these installations in Germany over space [...] Read more.
Information on geo-locations of renewable energy installations is very useful to investigate spatial, social or environmental questions on their impact at local and national level. However, existing data sets do not provide a sufficiently accurate representation of these installations in Germany over space and time. This work provides a valid approach on how a data set of wind power plants, photovoltaic field systems, bioenergy plants and hydropower plants can be created for Germany based on a data extract from the Core Energy Market Data Register (CEMDR) and publicly available data. Established methods were used (e.g., random forest, image recognition), but new techniques were also developed to fill data gaps or locate misplaced renewable energy installations. In this way, a substantial part of the CEMDR data could be corrected and processed in such a way that it can be freely used in a GIS software by any scientific and non-scientific discipline. Full article
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38 pages, 2500 KiB  
Article
Are Source Code Metrics “Good Enough” in Predicting Security Vulnerabilities?
by Sundarakrishnan Ganesh, Francis Palma and Tobias Olsson
Data 2022, 7(9), 127; https://doi.org/10.3390/data7090127 - 7 Sep 2022
Cited by 3 | Viewed by 1756
Abstract
Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write [...] Read more.
Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write code. In this study, we studied the ability of several machine learning algorithms to predict security vulnerabilities. We created two datasets containing security vulnerability information from two open-source systems: (1) Apache Tomcat (versions 4.x and five 2.5.x minor versions). We also computed source code metrics for these versions of both systems. We examined four classifiers, including Naive Bayes, Decision Tree, XGBoost Classifier, and Logistic Regression, to show their ability to predict security vulnerabilities. Moreover, an ensemble learner was introduced using a stacking classifier to see whether the prediction performance could be improved. We performed cross-version and cross-project predictions to assess the effectiveness of the best-performing model. Our results showed that the XGBoost classifier performed best compared to other learners, i.e., with an average accuracy of 97% in both datasets. The stacking classifier performed with an average accuracy of 92% in Struts and 71% in Tomcat. Our best-performing model—XGBoost—could predict with an average accuracy of 87% in Tomcat and 99% in Struts in a cross-version setup. Full article
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21 pages, 6222 KiB  
Article
Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells
by Marya Butt and Ander de Keijzer
Data 2022, 7(9), 126; https://doi.org/10.3390/data7090126 - 5 Sep 2022
Viewed by 2072
Abstract
Multiple blood images of stressed and sheared cells, taken by a Lorrca Ektacytometery microscope, needed a classification for biomedical researchers to assess several treatment options for blood-related diseases. The study proposes the design of a model capable of classifying these images, with high [...] Read more.
Multiple blood images of stressed and sheared cells, taken by a Lorrca Ektacytometery microscope, needed a classification for biomedical researchers to assess several treatment options for blood-related diseases. The study proposes the design of a model capable of classifying these images, with high accuracy, into healthy Red Blood Cells (RBCs) or Sickle Cells (SCs) images. The performances of five Deep Learning (DL) models with two different optimizers, namely Adam and Stochastic Gradient Descent (SGD), were compared. The first three models consisted of 1, 2 and 3 blocks of CNN, respectively, and the last two models used a transfer learning approach to extract features. The dataset was first augmented, scaled, and then trained to develop models. The performance of the models was evaluated by testing on new images and was illustrated by confusion matrices, performance metrics (accuracy, recall, precision and f1 score), a receiver operating characteristic (ROC) curve and the area under the curve (AUC) value. The first, second and third models with the Adam optimizer could not achieve training, validation or testing accuracy above 50%. However, the second and third models with SGD optimizers showed good loss and accuracy scores during training and validation, but the testing accuracy did not exceed 51%. The fourth and fifth models used VGG16 and Resnet50 pre-trained models for feature extraction, respectively. VGG16 performed better than Resnet50, scoring 98% accuracy and an AUC of 0.98 with both optimizers. The study suggests that transfer learning with the VGG16 model helped to extract features from images for the classification of healthy RBCs and SCs, thus making a significant difference in performance comparing the first, second, third and fifth models. Full article
(This article belongs to the Special Issue Knowledge Extraction from Data Using Machine Learning)
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10 pages, 480 KiB  
Data Descriptor
COVID-19 Lockdown Effects on Mood, Alcohol Consumption, Academic Functioning, and Perceived Immune Fitness: Data from Young Adults in Germany
by Anna Helin Koyun, Pauline A. Hendriksen, Pantea Kiani, Agnese Merlo, Jessica Balikji, Ann-Kathrin Stock and Joris C. Verster
Data 2022, 7(9), 125; https://doi.org/10.3390/data7090125 - 3 Sep 2022
Cited by 6 | Viewed by 2150
Abstract
Recently, a study was conducted in the Netherlands to evaluate the impact of the coronavirus disease (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates, such as alcohol consumption. The Dutch study revealed that lockdowns were associated with [...] Read more.
Recently, a study was conducted in the Netherlands to evaluate the impact of the coronavirus disease (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates, such as alcohol consumption. The Dutch study revealed that lockdowns were associated with significantly poorer mood and reductions in perceived immune fitness. Overall, a reduction in alcohol consumption during lockdown periods was shown. Academic functioning in terms of self-reported performance was unaffected. However, a significant reduction in interactions with other students and teachers was reported. However, there was considerable variability among students; both increases and reductions in alcohol consumption were reported, as well as both improvements and poorer academic functioning during periods of lockdown. The aim of the current online study was to replicate these findings in Germany. To achieve this, a slightly modified version of the survey was administered among young adults (aged 18 to 35 years old) in Germany. The survey assessed possible changes in self-reported academic functioning, mood, and health correlates, such as smoking and alcohol consumption, perceived immune functioning, and sleep quality during periods of lockdown as compared to periods with no lockdowns. Retrospective assessments were made for five periods, including (1) ‘BP’ (the period before the COVID-19 pandemic), (2) ‘L1’ (the first lockdown period, March–May 2020), (3) ‘NL1’ (the first no-lockdown period, summer 2020), (4) ‘L2’ (the second lockdown, November 2020 to May 2021), and (5) ‘NL2’ (the second no-lockdown period, summer 2021). This article describes the content of the survey and the corresponding dataset. The survey was completed by 371 participants. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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7 pages, 244 KiB  
Data Descriptor
An Updated List of Rock Partridge (Alectoris graeca) Haplotypes from the Apennines—Central Italy
by Leonardo Brustenga, Paolo Viola, Pedro Girotti, Andrea Amici, Alessandro Rossetti, Stefania Chiesa, Riccardo Primi, Luigi Esposito and Livia Lucentini
Data 2022, 7(9), 124; https://doi.org/10.3390/data7090124 - 1 Sep 2022
Viewed by 1554
Abstract
We report an updated and expanded list of Rock Partridge (Alectoris graeca) haplotypes found in wild animals throughout the Apennines of central Italy. Samples were collected and identified during a monitoring program of autochthonous Galliformes and from a private collection. The [...] Read more.
We report an updated and expanded list of Rock Partridge (Alectoris graeca) haplotypes found in wild animals throughout the Apennines of central Italy. Samples were collected and identified during a monitoring program of autochthonous Galliformes and from a private collection. The haplotypes were identified on a longer fragment of the mitochondrial control region (D-loop) based on previously reported haplotypes. This novel evidence, based on a wider sampling area and a higher number of analyzed specimens, will be of relevance in both conservation projects and gamebird breeding for restock, as imposed by the Italian Action Plan. Studying longer fragments can also be useful for phylogeographic analysis. Full article
14 pages, 2831 KiB  
Article
Revealing the Complete Chloroplast Genome of an Andean Horticultural Crop, Sweet Cucumber (Solanum muricatum), and Its Comparison with Other Solanaceae Species
by Carla L. Saldaña, Julio C. Chávez-Galarza, Germán De la Cruz, Jorge H. Jhoncon, Juan C. Guerrero-Abad, Héctor V. Vásquez, Jorge L. Maicelo and Carlos I. Arbizu
Data 2022, 7(9), 123; https://doi.org/10.3390/data7090123 - 1 Sep 2022
Cited by 2 | Viewed by 2591
Abstract
Sweet cucumber (Solanum muricatum) sect. Basarthrum is a neglected horticultural crop native to the Andean region. It is naturally distributed very close to other two Solanum crops of high importance, potatoes, and tomatoes. To date, molecular tools for this crop remain undetermined. In [...] Read more.
Sweet cucumber (Solanum muricatum) sect. Basarthrum is a neglected horticultural crop native to the Andean region. It is naturally distributed very close to other two Solanum crops of high importance, potatoes, and tomatoes. To date, molecular tools for this crop remain undetermined. In this study, the complete sweet cucumber chloroplast (cp) genome was obtained and compared with seven Solanaceae species. The cp genome of S. muricatum was 155,681 bp in length and included a large single copy (LSC) region of 86,182 bp and a small single-copy (SSC) region of 18,360 bp, separated by a pair of inverted repeats (IR) regions of 25,568 bp. The cp genome possessed 87 protein-coding genes (CDS), 37 transfer RNA (tRNA) genes, eight ribosomal RNA (rRNA) genes, and one pseudogene. Furthermore, 48 perfect microsatellites were identified. These repeats were mainly located in the noncoding regions. Whole cp genome comparative analysis revealed that the SSC and LSC regions showed more divergence than IR regions. Similar to previous studies, our phylogenetic analysis showed that S. muricatum is a sister species to members of sections Petota + Lycopersicum + Etuberosum. We expect that this first sweet cucumber chloroplast genome will provide potential molecular markers and genomic resources to shed light on the genetic diversity and population studies of S. muricatum, which will allow us to identify varieties and ecotypes. Finally, the features and the structural differentiation will provide us with information about the genes of interest, generating tools for the most precise selection of the best individuals of sweet cucumber, in less time and with fewer resources. Full article
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13 pages, 695 KiB  
Article
Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques
by Fairouz Hussein, Ayat Al-Ahmad, Subhieh El-Salhi, Esra’a Alshdaifat and Mo’taz Al-Hami
Data 2022, 7(9), 122; https://doi.org/10.3390/data7090122 - 31 Aug 2022
Cited by 3 | Viewed by 6799
Abstract
Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such [...] Read more.
Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial. Full article
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8 pages, 2075 KiB  
Data Descriptor
Transcriptome Profiles of Circular RNAs in Common Wheat during Fusarium Head Blight Disease
by Junliang Yin, Xiaowen Han, Yongxing Zhu, Zhengwu Fang, Derong Gao and Dongfang Ma
Data 2022, 7(9), 121; https://doi.org/10.3390/data7090121 - 29 Aug 2022
Cited by 3 | Viewed by 2071
Abstract
Circular RNAs (circRNAs) are covalently closed RNA molecules, and have been identified in many crops. However, there are few datasets for circRNA junctions from common wheat during Fusarium head blight disease. In the present study, we used RNA-seq to determine the changes in [...] Read more.
Circular RNAs (circRNAs) are covalently closed RNA molecules, and have been identified in many crops. However, there are few datasets for circRNA junctions from common wheat during Fusarium head blight disease. In the present study, we used RNA-seq to determine the changes in circRNAs among the control (CK) and 1, 3, and 5 days post-Fusarium graminearum inoculation (dpi) samples. More than one billion reads were produced from 12 libraries, and 99.99% of the reads were successfully mapped to a wheat reference genome. In total, 2091 high-confidence circRNAs—which had two or more junction reads and were supported by at least two circRNA identification algorithms—were detected. The completed expression profiling revealed a distinct expression pattern of circRNAs among the CK, 1dpi, 3dpi and 5dpi samples. This study provides a valuable resource for identifying F. graminearum infection-responsive circRNAs in wheat and for further functional characterization of circRNAs that participated in the Fusarium head blight disease response of wheat. Full article
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8 pages, 219 KiB  
Data Descriptor
A Dataset for the Vietnamese Banking System (2002–2021)
by Tu D. Q. Le, Tin H. Ho, Thanh Ngo, Dat T. Nguyen and Son H. Tran
Data 2022, 7(9), 120; https://doi.org/10.3390/data7090120 - 25 Aug 2022
Cited by 9 | Viewed by 8318
Abstract
This data article describes a dataset that consists of key statistics on the activities of 45 Vietnamese banks (e.g., deposits, loans, assets, and labor productivity), operated during the 2002–2021 period, yielding a total of 644 bank-year observations. This is the first systematic compilation [...] Read more.
This data article describes a dataset that consists of key statistics on the activities of 45 Vietnamese banks (e.g., deposits, loans, assets, and labor productivity), operated during the 2002–2021 period, yielding a total of 644 bank-year observations. This is the first systematic compilation of data on the splits of state vs. private ownership, foreign vs. domestic banks, commercial vs. policy banks, and listed vs. nonlisted banks. Consequently, this arrives at a unique set of variables and indicators that allow us to capture the development and performance of the Vietnamese banking sector over time along many different dimensions. This can play an important role for financial analysts, researchers, and educators in banking efficiency and performance, risk and profit/revenue management, machine learning, and other fields. Full article
(This article belongs to the Special Issue Second Edition of Data Analysis for Financial Markets)
17 pages, 1326 KiB  
Data Descriptor
Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education
by Joanna Alvarado-Uribe, Paola Mejía-Almada, Ana Luisa Masetto Herrera, Roland Molontay, Isabel Hilliger, Vinayak Hegde, José Enrique Montemayor Gallegos, Renato Armando Ramírez Díaz and Hector G. Ceballos
Data 2022, 7(9), 119; https://doi.org/10.3390/data7090119 - 25 Aug 2022
Cited by 10 | Viewed by 7488
Abstract
High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify [...] Read more.
High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students. Full article
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